mdbloice/Augmentor
Augmentor
Augmentor هي مكتبة وإطار عمل لتعزيز الصور بلغة Python مصمم لتوسيع مجموعات بيانات التعلم الآلي. تعمل كأداة معالجة مسبقة تولد تنويعات صور اصطناعية لزيادة تنوع البيانات، وكأداة بث لبيانات التدريب التي تغذي الصور المعززة والتسميات مباشرة في حلقات الشبكة العصبية دون الحاجة إلى تخزين قرص وسيط.
يحافظ إطار العمل على المحاذاة المكانية بين الصور والأقنعة المقابلة لها، وهو أمر مطلوب لتدريب التجزئة الدلالية. ويدعم تحويلات هندسية وعلى مستوى البكسل متنوعة، بما في ذلك التشوهات المرنة، وتحولات المنظور عبر الإمالة، والتدوير، والقص، ومسح المناطق العشوائي.
يتضمن النظام إمكانيات لاستراتيجيات المعالجة لكل فئة لمعالجة عدم توازن البيانات، ويستخدم تعدد الخيوط لتسريع التوليد المتوازي لمجموعات البيانات المعززة. كما يوفر أدوات لتنظيف وتوحيد ملفات الصور الخام خلال مرحلة المعالجة المسبقة.
Features
- Computer Vision Dataset Expansion - Increases training set size and diversity by generating multiple synthetic variations of images.
- Image Augmentation - Applies geometric distortions and random erasures to images to improve vision model robustness.
- Transform Sequence Assemblers - Implements sequences of transformations to generate augmented data from a single input image.
- Image Data Preprocessing - Provides utilities to clean and standardize raw image files for use in machine learning processing pipelines.
- Image Dataset Expansion Frameworks - Provides a framework to expand image datasets using synchronized transformations for images and masks.
- ML Training Data Streamers - Streams augmented images and labels directly into training loops to avoid storing intermediate files.
- On-the-Fly Training Transformations - Streams augmented images and labels directly into training loops to avoid writing intermediate files to disk.
- Synchronized Label Augmentations - Applies identical geometric transformations to images and labels to maintain spatial alignment.
- Image Augmentations - A Python library for generating synthetic image variations to increase training dataset diversity.
- Training Sample Streaming - Streams augmented image batches and labels directly into neural network training loops.
- Geometric Mask Synchronization - Ensures that spatial coordinates and rotation parameters are applied identically to both images and their label masks.
- Class-Specific Augmentation Strategies - Allows defining unique processing pipelines for different object categories to handle data imbalances.
- Per-Class Processing Configurations - Provides a configuration system for unique processing pipelines tailored to different object categories.
- Computer Vision Preprocessing - Provides utilities for cleaning and standardizing raw image files to prepare them for training.
- Region Erasers - Implements random region erasing to obscure portions of images, improving model robustness against occlusions.
- Semantic Segmentation Training - Maintains spatial alignment between images and masks during transformations for pixel-level labeling.
- Parallel Image Generation - Uses multi-threading to distribute image processing tasks across CPU cores for faster dataset generation.
- Rotation and Shear transforms - Provides capabilities to rotate and shear images while cropping results to maintain consistent dimensions.
- Coordinate-Based Spatial Distortions - Provides spatial distortions like tilt, skew, and elastic warping to simulate various camera perspectives.
- Elastic Distortion Augmentations - Implements elastic distortions to create realistic image variations while preserving labels and aspect ratios.
- Perspective Warping - Provides geometric transformations to alter image viewpoints and simulate different camera angles through tilt and skew.
- Parallel Image Transformers - Uses multi-threading to accelerate the parallel generation and saving of augmented image datasets across CPU cores.
- رؤية الحاسوب - Image augmentation library.
- Data Wrangling - User-friendly tool for image augmentation in classification tasks.
- أدوات المطور - Python library for image augmentation in machine learning pipelines.
